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          <h1 class="post-title" itemprop="name headline">ETF定投数据分析4——计算各种回测指标</h1>
        

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        <p>先计算各种回测和评估策略时要用的指标吧。<br>参考:<br><a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/24356155">https://zhuanlan.zhihu.com/p/24356155</a><br><a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/55425806">https://zhuanlan.zhihu.com/p/55425806</a><br><a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/29386150">https://zhuanlan.zhihu.com/p/29386150</a><br>先建立一个新的分支Index，再建立一个新文件index.py，写计算各种指标的程序。<br>这个过程折腾了我几天，方法是先建立一个testpaper.py的文件，复制上面第二篇文章中的代码，直到调通，得到跟作者一致的结果。然后再把各个指标的计算方法放到各自的函数里，最后再写一个总的调用各个指标计算方法的函数。做成一个index库。调通以后，再用文章中的数据来检验库的计算是否正确(的确发现几个bug)。另外因为我的数据是收益率数据(因为成本在变化，收益率能涵盖成本变化，而市值等数据貌似不行)，计算年化收益率和最大回撤的方法跟作者不一样。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#计算期间年化收益率，用原始数据</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">GetAR</span>(<span class="params">data</span>):</span></span><br><span class="line">    ar = (<span class="number">1</span>+data[<span class="string">&quot;数据&quot;</span>].iloc[-<span class="number">1</span>])**(<span class="number">250.0</span>/<span class="built_in">len</span>(data[<span class="string">&quot;数据&quot;</span>]))-<span class="number">1</span></span><br><span class="line">    <span class="keyword">return</span> ar</span><br><span class="line">   </span><br><span class="line"></span><br><span class="line"><span class="comment">#计算最大回撤，因为数据已经是收益率了，直接减就行了。用原始数据。</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">GetMD</span>(<span class="params">data</span>):</span></span><br><span class="line">    md = (data[<span class="string">&quot;数据&quot;</span>].cummax() - data[<span class="string">&quot;数据&quot;</span>]).<span class="built_in">max</span>()</span><br><span class="line">    <span class="keyword">return</span> <span class="built_in">round</span>(md, <span class="number">4</span>)</span><br><span class="line">   </span><br><span class="line">   </span><br><span class="line"><span class="comment">#计算β和α系数</span></span><br><span class="line"><span class="comment">#因为是指数定投，不存在停盘等数据缺失的问题</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">AlphaBeta</span>(<span class="params">data, basedata</span>):</span></span><br><span class="line">    x = basedata[<span class="string">&quot;数据&quot;</span>].values <span class="comment">#基准数据</span></span><br><span class="line">    y = data[<span class="string">&quot;数据&quot;</span>].values <span class="comment">#要评价的数据</span></span><br><span class="line">    b,a,r_value,p_value,std_err = stats.linregress(x, y)</span><br><span class="line">    <span class="comment">#print(b, a, r_value, p_value, std_err)</span></span><br><span class="line">    a = <span class="built_in">round</span>(a*<span class="number">250</span>, <span class="number">3</span>)</span><br><span class="line">    AB = [a, <span class="built_in">round</span>(b, <span class="number">3</span>)]</span><br><span class="line">    <span class="keyword">return</span> (AB)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment">#计算夏普比率,saferate为无风险收益，用余额宝收益吧。</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">Sharpe</span>(<span class="params">data, saferate</span>):</span></span><br><span class="line">    exReturn = data[<span class="string">&quot;数据&quot;</span>] - saferate/<span class="number">250.0</span></span><br><span class="line">    sharperatio = np.sqrt(<span class="built_in">len</span>(exReturn))*exReturn.mean()/exReturn.std()</span><br><span class="line">    <span class="comment">#print(sharperatio)</span></span><br><span class="line">    <span class="keyword">return</span> sharperatio</span><br><span class="line">   </span><br><span class="line">   </span><br><span class="line"><span class="comment">#计算信息比率</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">Information</span>(<span class="params">data, basedata</span>):</span></span><br><span class="line">    ex_return = data[<span class="string">&quot;数据&quot;</span>] - basedata[<span class="string">&quot;数据&quot;</span>]</span><br><span class="line">    information = np.sqrt(<span class="built_in">len</span>(ex_return))*ex_return.mean()/ex_return.std()</span><br><span class="line">    <span class="comment">#print(information)</span></span><br><span class="line">    <span class="keyword">return</span> information</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="string">&#x27;&#x27;&#x27;计算各指标的总函数，以后用户就调这个就行了。</span></span><br><span class="line"><span class="string">data为要计算的策略的数据，basedata为基准数据。</span></span><br><span class="line"><span class="string">safeIncome为无风险收益率，暂用余额宝的3%吧&#x27;&#x27;&#x27;</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">index</span>(<span class="params">data, basedata, safeIncome</span>):</span></span><br><span class="line">    AR = GetAR(data) <span class="comment">#计算年化收益率</span></span><br><span class="line">    MD = GetMD(data) <span class="comment">#计算最大回撤</span></span><br><span class="line">    AB = AlphaBeta(data, basedata) <span class="comment">#计算αβ系数</span></span><br><span class="line">    SR = Sharpe(data, safeIncome) <span class="comment">#计算夏普系数</span></span><br><span class="line">    IR = Information(data, basedata)</span><br><span class="line">    result = [AR, MD, AB[<span class="number">0</span>], AB[<span class="number">1</span>], SR, IR]</span><br><span class="line">    df_result = pd.Series(data = result, index =[<span class="string">&quot;年化收益率&quot;</span>, <span class="string">&quot;最大回撤&quot;</span>, <span class="string">&quot;α系数&quot;</span>, <span class="string">&quot;β系数&quot;</span>, <span class="string">&quot;夏普系数&quot;</span>, <span class="string">&quot;信息比率&quot;</span>])</span><br><span class="line">    <span class="keyword">return</span> df_result</span><br></pre></td></tr></table></figure>
<p>完整代码就不贴了，去看我的github项目主页吧: <a target="_blank" rel="noopener" href="https://github.com/zwdnet/etfdata">https://github.com/zwdnet/etfdata</a><br>最后用我实际定投数据算出的指标为</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">年化收益率     -<span class="number">0.114184</span></span><br><span class="line">最大回撤       <span class="number">0.159400</span>                                     </span><br><span class="line">α系数      -<span class="number">42.816000</span>                                       </span><br><span class="line">β系数        <span class="number">0.044000</span>                                       </span><br><span class="line">夏普系数      -<span class="number">6.646816</span>                                     </span><br><span class="line">信息比率    -<span class="number">186.269012</span></span><br></pre></td></tr></table></figure>
<p>任何夏普比例低于1的策略都不适合单独使用，几乎每月都实现盈利的策略，其年化夏普比率通常都大于2；几乎每天盈利的策略，其夏普比率通常大于3。（《量化交易 如何建立自己的算法交易》 p18）<br>夏普比率越高，该策略在每单位风险（标准差）上创造出的超额收益就越多。<br>α表示了一个策略风险调整后的超额收益。与夏普比率不同之处是它使用β来表示风险。<br>(《量化投资策略：如何实现超额收益》 p22)<br>可见我的策略实盘并不好，接下来就利用这些函数开始折腾数据啦。<br>我发文章的三个地方，欢迎大家在朋友圈等地方分享，欢迎点“在看”。<br>我的个人博客地址：<a href="https://zwdnet.github.io/">https://zwdnet.github.io</a><br>我的知乎文章地址： <a target="_blank" rel="noopener" href="https://www.zhihu.com/people/zhao-you-min/posts">https://www.zhihu.com/people/zhao-you-min/posts</a><br>我的微信个人订阅号：赵瑜敏的口腔医学学习园地</p>
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